Microsoft Azure AI Fundamentals (AI-900) Practice Exam

What is the process of tagging training data with known values called?

1. Labeling

2. Segmentation

3. Tagging

4. Annotation

The correct answer is: Labeling

The process of tagging training data with known values is referred to as labeling. Labeling is a fundamental step in supervised machine learning, where specific pieces of data are identified with corresponding output values or categories. This known information allows machine learning models to learn patterns and make predictions or classifications based on new, unseen data. In this context, labeled data serves as a crucial foundation for training algorithms, as it directly informs the model about the relationships between input features and the target outputs. Thus, effective labeling is essential for developing accurate AI systems, enabling them to generalize from the training data to make predictions in real-world scenarios. Other terms such as segmentation and annotation sometimes appear in related contexts but serve different functions. Segmentation is more commonly used in image processing to divide an image into meaningful parts, while annotation typically denotes the action of adding notes or comments to existing data rather than the structured labeling needed for training algorithms. Tagging can be a broader term that does not always specifically imply the systematic and structured output that labeling conveys in a machine learning context.